The RLSSR module takes the output text embedding, \(T\)  from the last layer of the decoder and the output audio embedding, \(A\) , from the CNN from the encoder as input. A average max pooling is applied to \(A\)  to extract the most prominent features to be reconstructed. After which, an average global pooling is applied to standardize the length of the audio features to obtain Apooled. The text embeddings \(T\)  are first passed through a linear layer and then an average global pooling layer to obtain the reconstructed representation from the text, Arec. Finally, a similarity metric is used to minimize the distance between Apooled and Arec. These operations are shown in the Equation REF . While we did try other similarity metrics such as cosine similarity, Huber loss [1]}, we found that the L1 distance, L2 distance worked the best as the similarity metric.
\(\begin{array}{l}L = \alpha L_{ce} + \beta L_{RLLSR} \\\end{array}\) 
